OpenSALICON is an open source implementation of the SALICON saliency model cited below. Huang, X., Shen, C., Boix, X., & Zhao, Q. (2015). SALICON: Reducing the Semantic Gap in Saliency Prediction by Adapting Deep Neural Networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 262-270): Saliency in Context (SALICON) is an ongoing effort that aims at understanding and predicting visual attention. Conventional saliency models typically rely on low-level image statistics to predict human fixations. While these models perform significantly better than chance, there is still a large gap between model prediction and human behavior. This gap is largely due to the limited capability of models in predicting eye fixations with strong semantic content, the so-called semantic gap. This paper presents a focused study to narrow the semantic gap with an architecture based on Deep Neural Network (DNN). It leverages the representational power of high-level semantics encoded in DNNs pretrained for object recognition. Two key components are fine-tuning the DNNs with an objective function based on the saliency evaluation metrics, and integrating information at different image scales. We compare our method with 14 saliency models on 6 public eye tracking benchmark datasets. Results demonstrate that our DNNs can automatically learn features for saliency prediction that surpass by a big margin the state-of-the-art. In addition, our model ranks top to date under all seven metrics on the MIT300 challenge set.
Keywords for this software
References in zbMATH (referenced in 2 articles )
Showing results 1 to 2 of 2.
- Ehret, Thibaud; Davy, Axel; Morel, Jean-Michel; Delbracio, Mauricio: Image anomalies: a review and synthesis of detection methods (2019)
- Calden Wloka, Toni Kunić, Iuliia Kotseruba, Ramin Fahimi, Nicholas Frosst, Neil D. B. Bruce, John K. Tsotsos: SMILER: Saliency Model Implementation Library for Experimental Research (2018) arXiv